Data Lake vs Data Warehouse: What’s the Difference?

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Quick Hits 
  • A data lake is a data storage solution designed for easy storage of both structured and unstructured files in their native formats.  
  • A data warehouse is a data storage solution designed for highly organized storage of data transformed to fit a structure that supports strategic analysis. 
  • Most companies will utilize both data lakes and data warehouses depending on the type and utility of the data they are storing.  

The amount of data an organization has the capacity to generate is astounding. Storing this data can be a complicated process, especially when some data needs to be easily accessible, some data needs to be stored indefinitely, and some data needs to be carefully organized to be useful. Most businesses will end up using both data lakes and data warehouses to store their data depending on the role that data has to play in their organization. 

In order to decide how and where files should be stored, let’s take a look at the differences between data lakes and data warehouses, and the benefits of each system. 

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What is a Data Lake? 

Just like a lake has many sources of water that are eventually collected in the same space, a data lake is a collection of files all swept up and stored together in the same place.  

Data lakes can store any kind of data, and cloud-based data lakes have become especially popular as businesses gain more and more files that need to be stored. Data lakes can store structured, semi-structured, and unstructured data as it stores files in their original formats. 

Data lakes have scalable storage capacity and don’t limit file sizes or types. This makes them the ideal solution for most data storage. Usually, files are given useful tags so they can be easily searched when a certain file is needed. 

Data lakes are the preferred solution for files that are stored because of their independent contents, not necessarily as the data they contain as a group. Depending on the kind of files stored within a data lake, they can have a wide range of analytic uses. Data is stored quickly and easily, with little forethought needed.  

What is a Data Warehouse? 

If a data lake is an unorganized flow of data all coming to rest in the same space, a data warehouse is a highly organized building full of filing cabinets working as a cohesive system. Data is stored in organized files and folders according to specialized systems that assist with strategic insights. 

Most data warehouses work with structured data, or data transformed out of its original form for querying purposes. A data warehouse is a highly structured environment designed to glean strategic insights or for specialized data usage.  

Data warehouses are preferable for transforming data into useful insights and information. Consider data warehouses as the storage for simple factual data (52% of those surveyed like navy blue) instead of nuanced data (like the “additional comments” section of a survey) that needs to be read by a human instead of a machine. 

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Key Differences 

While most companies utilize both data lakes and data warehouses, it may be hard to narrow down what data should be stored where. Looking at these key differences should help you understand where, and how, to store the data you’re working with.  

Native Format vs Transformed Format 

When choosing where to store data, one of the most helpful questions is whether the data needs to be stored in its native format or not. If a file must be stored in its native format, a data lake is an easy choice.

If the data is being used for an express analytical purpose, then a data warehouse will probably be the best choice. If the data doesn’t need to be stored in its original format and isn’t being used for an express purpose yet, storing it in a data lake for later use is a smart move. 

Unorganized vs Organized  

Data lakes tend to store large amounts of data, much of which does not have a predetermined use or reason for storage. Data lakes can quickly become disorganized masses of files without proper precautions. Even at their most organized, data lakes tend to be more disorganized than data warehouses.  

Data warehouses are strictly organized, with every piece of data serving a particular purpose. Data warehouses are usually organized with an express purpose in mind, which means the organizational structure usually adds to the utility of the data. 

Large Storage Capacity vs Selective Data Storage 

Unorganized native files tend to take up more space than simplified organized data. Data lakes usually offer more storage space than data warehouses, even offering scalable storage solutions.

Data warehouses will usually take up less space since they don’t include extraneous data or files as a part of their system. For companies that need a larger amount of storage, cloud data warehouse solutions enable companies to manage large amounts of data easily.

Complex Analysis vs Simple Analysis 

Data lakes have so much information, often in differing formats, that mass analysis is difficult. Usually, it takes a skilled data scientist to navigate a data lake and pull out insights.  

Data warehouses translate data into statistics and insights that are easier to analyze. Though organization may take more work upfront, the average businessperson can utilize the data stored in a data warehouse.  

Whether you utilize both data lakes and data warehouses or make do with only one storage space, understanding these storage solutions will help you better manage your data. Data can be a fantastic tool, but without proper organization, its strategic benefits are easily lost.  

If you are storing and managing sensitive data, like cardholder data or personally identifiable information (PII), consider using a tokenization platform to secure your data. Tokenization swaps sensitive data for tokens in order to secure the data outside of internal systems. Tokens are designed to preserve data’s utility and alleviate the burdens of compliance.